The impact of organizational stigmatization on the operational risk and performance of overseas subsidiaries: empirical evidence from Chinese multinational enterprises

This study used the global database of events, language, and tone of international public opinion big data to measure organizational stigmatization against China. It then used an econometric model to investigate the impact of organizational stigmatization on the operational risk and performance of overseas subsidiaries of Chinese multinational enterprises. The results show that: (1) organizational stigmatization increases overseas subsidiaries’ operational risk and reduces their operational performance, which is more evident in overseas subsidiaries of state-owned enterprises; (2) the host country’s political stability weakens the organizational stigmatization’s positive impact on overseas subsidiaries’ operational risk. The geographical distance between the home and host countries strengthens organizational stigmatization’s positive impact on overseas subsidiaries’ operational risk; (3) the host country’s political stability and the geographical distance between the home and host countries have no moderating effect on organizational stigmatization and overseas subsidiaries’ operational performance; and (4) organizational stigmatization by the host country reduces overseas subsidiaries’ operational performance via the channel of operational risk. This study innovates the measurement method of organizational stigmatization and lays the foundation for investigating the microeconomic impact of organizational stigmatization from the perspective of overseas subsidiaries

E2E-LOAD: End-to-End Long-form Online Action Detection

Recently, there has been a growing trend toward feature-based approaches for Online Action Detection (OAD). However, these approaches have limitations due to their fixed backbone design, which ignores the potential capability of a trainable backbone. In this paper, we propose the first end-to-end OAD model, termed E2E-LOAD, designed to address the major challenge of OAD, namely, long-term understanding and efficient online reasoning. Specifically, our proposed approach adopts an initial spatial model that is shared by all frames and maintains a long sequence cache for inference at a low computational cost. We also advocate an asymmetric spatial-temporal model for long-form and short-form modeling effectively. Furthermore, we propose a novel and efficient inference mechanism that accelerates heavy spatial-temporal exploration. Extensive ablation studies and experiments demonstrate the effectiveness and efficiency of our proposed method. Notably, we achieve 17.3 (+12.6) FPS for end-to-end OAD with 72.4%~(+1.2%), 90.3%~(+0.7%), and 48.1%~(+26.0%) mAP on THMOUS14, TVSeries, and HDD, respectively, which is 3x faster than previous approaches. The source code will be made publicly available

Learning Point-Language Hierarchical Alignment for 3D Visual Grounding

This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner. We extract key points and proposal points to model 3D contexts and instances, and propose point-language alignment with context modulation (PLACM) mechanism, which learns to gradually align word-level and sentence-level linguistic embeddings with visual representations, while the modulation with the visual context captures latent informative relationships. To further capture both global and local relationships, we propose a spatially multi-granular modeling scheme that applies PLACM to both global and local fields. Experimental results demonstrate the superiority of HAM, with visualized results showing that it can dynamically model fine-grained visual and linguistic representations. HAM outperforms existing methods by a significant margin and achieves state-of-the-art performance on two publicly available datasets, and won the championship in ECCV 2022 ScanRefer challenge. Code is available at~\url{https://github.com/PPjmchen/HAM}.Comment: Champion on ECCV 2022 ScanRefer Challeng

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis

Cross-sectional optimization of cold-formed steel channels to Eurocode 3

Cold-formed steel structural systems are widely used in modern construction. However, identifying optimal cross section geometries for cold-formed steel elements is a complex problem, since the strength of these members is controlled by combinations of local, distortional, and global buckling. This paper presents a procedure to obtain optimized steel channel cross-sections for use in compression or bending. A simple lipped C-shape is taken as a starting point, but the optimization process allows for the addition of double-fold (return) lips, inclined lips and triangular web stiffeners. The cross-sections are optimized with respect to their structural capacity, determined according to the relevant Eurocode (EN1993-1-3), using genetic algorithms. All plate slenderness limit values and all limits on the relative dimensions of the cross-sectional components, set by the Eurocode, are thereby taken into account as constraints on the optimization problem. The optimization for compression is carried out for different column lengths and includes the effects of the shift of the effective centroid induced by local buckling. Detailed finite element models are used to confirm the relative gains in capacity obtained through the optimization process

MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning

Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.Comment: 21 pages, 9 figures, 10 table

Family function and adolescent altruistic behavior: the multiple mediating effects of self-affirmation and psychological resilience

IntroductionThe current study aimed to explore the relationship between family function and adolescent altruistic behavior, as well as the mediating effects of self-affirmation and psychological resilience in this relationship.MethodsA survey was conducted on 972 high school students in Guangdong Province using the Family APGAR, GHQSense of Adequacy, Chinese version of Connor-Davidson Resilience Scale, and Altruistic Behavior Scale.ResultsResults found that the score of psychological resilience of males was significantly higher than that of females, but the score of altruistic behavior was significantly lower than that of females. Family function had a positive predictive effect on altruistic behavior. Psychological resilience played a mediating role between family function and altruistic behavior. Self-affirmation and psychological resilience played chain mediating roles between family function and altruistic behavior.DiscussionThis study indicated that family care is crucial for the development of adolescent altruistic behavior, and that it can promote the development of altruistic behavior through the enhancement of self-affirmation and psychological resilience

Cross-sectional optimization of cold-formed steel channels to Eurocode 3

Cold-formed steel structural systems are widely used in modern construction. However, identifying optimal cross section geometries for cold-formed steel elements is a complex problem, since the strength of these members is controlled by combinations of local, distortional, and global buckling. This paper presents a procedure to obtain optimized steel channel cross-sections for use in compression or bending. A simple lipped C-shape is taken as a starting point, but the optimization process allows for the addition of double-fold (return) lips, inclined lips and triangular web stiffeners. The cross-sections are optimized with respect to their structural capacity, determined according to the relevant Eurocode (EN1993-1-3), using genetic algorithms. All plate slenderness limit values and all limits on the relative dimensions of the cross-sectional components, set by the Eurocode, are thereby taken into account as constraints on the optimization problem. The optimization for compression is carried out for different column lengths and includes the effects of the shift of the effective centroid induced by local buckling. Detailed finite element models are used to confirm the relative gains in capacity obtained through the optimization process

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis